CausalMVC: Causal content-style representation learning for deep multi-view clustering

Multi-view clustering aims to extract and integrate semantic information from multiple views to improve clustering performance. While deep learning-based approaches have shown promising results, they suffer from noisy view dependency (NVD) and dominant view dependency (DVD), limiting their robustness and effectiveness. NVD arises when models fail to filter out irrelevant variations, treating noise as semantic information. DVD occurs when models over-rely on dominant views, neglecting complementary information from other perspectives. To address these challenges, we propose causal content-style representation learning for deep multi-view clustering. To mitigate NVD, we incorporate causal content-style disentanglement via a dual differential content-style network for separation of semantic information from noise. Meanwhile, to reduce DVD, we introduce causal content consistency that aligns semantic content from both intra-view and cross-view perspectives. Besides, we design a content-centered style receptive field for contrastive learning, enhancing the semantic association between positive sample pairs while preventing over-alignment to dominant views. Extensive experiments on ten benchmark datasets demonstrate that CausalMVC outperforms state-of-the-art methods, validating its effectiveness.